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Achieving Energy Efficiency in Industrial Manufacturing
Scania CV AB, Global Industrial Development, Södertälje, 151 38, Sweden; Uppsala University, Department of Civil and Industrial Engineering, Division of Industrial Engineering & Management, Uppsala, 752 37, Sweden.ORCID-id: 0000-0001-9747-5608
RISE Research Institutes of Sweden AB, Mölndal, 431 50, Sweden.ORCID-id: 0000-0001-8694-4122
KTH, Skolan för industriell teknik och management (ITM), Produktionsutveckling, Avancerade underhållssystem och produktionslogistik.ORCID-id: 0000-0003-0798-0753
University of Skövde, School of Engineering Science, Skövde, 541 28, Sweden.ORCID-id: 0000-0002-7232-9353
2025 (engelsk)Inngår i: Renewable & sustainable energy reviews, ISSN 1364-0321, E-ISSN 1879-0690, Vol. 216, artikkel-id 115619Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

This paper explores the use of digital technology stages and knowledge demand types for achieving energy efficiency. Digital technology stages are the steps toward developing an intelligent and networked factory: computerization, connectivity, visibility, transparency, predictive capacity, and adaptability. Knowledge demand types refer to the knowledge and skills needed to implement energy management through technical, process, and leadership knowledge. Empirical data were collected from a critical single case study at an industrial manufacturing company. The study made two significant contributions. Firstly, it identifies fourteen challenges and improvement potentials when working with energy monitoring, evaluation, and optimization, demonstrating the critical role of digital technology stages and knowledge demand types. Secondly, the study presents a conceptual framework indicating how companies could overcome pitfalls and enhance energy efficiency by combining digital technologies and knowledge demands. Future work will include technical implementations and its connection to knowledge management.

sted, utgiver, år, opplag, sider
Elsevier BV , 2025. Vol. 216, artikkel-id 115619
Emneord [en]
Energy efficiency, Energy waste, Energy management, Technology use, Knowledge demands, Manufacturing
HSV kategori
Forskningsprogram
Industriell ekonomi och organisation
Identifikatorer
URN: urn:nbn:se:kth:diva-361803DOI: 10.1016/j.rser.2025.115619ISI: 001488956300001Scopus ID: 2-s2.0-105000946035OAI: oai:DiVA.org:kth-361803DiVA, id: diva2:1948139
Prosjekter
Explainable and Learning Production & Logistics by Artificial Intelligence
Forskningsfinansiär
Vinnova, 2021-01289
Merknad

QC 20250331

Tilgjengelig fra: 2025-03-27 Laget: 2025-03-27 Sist oppdatert: 2025-07-03bibliografisk kontrollert

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Flores-García, Erik

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